Computer Vision (CV) algorithms are used in a variety of applications such as surveillance, smart home, autonomous driving, just to name a few. Many CV algorithms rely on background subtraction to identify moving objects. In such algorithms, a background model is first generated, which is then used to identify objects. Since the background model changes, due to the changes in lighting, movement of background objects such as chairs and so forth, the background model has to be constantly updated.
Other sensors employ similar algorithms to subtract background sensed information, although the range of background model changes for non-optical sensors is generally more limited than in CV applications. However, whether the sensors or detectors are configured to detect light, sound or heat, many such detectors rely on comparing the light, sound or heat of a new object to the ambient or background conditions, or to some other reference value. Many such sensors or detectors rely on converting an analog signal from the sensor to a digital signal, and it is this digital signal that is compared between newly detected conditions and background information. The number and density of the sensors can mean that the amount of data being transmitted and compared can be very large. Performing background comparisons for such large amounts of data can be unwieldy and slow, and even prohibitive in a worst case. Thus, there is a need for a system in an attempt to reduce data rate and also power consumption.